AI RESEARCH
DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference
arXiv CS.CL
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ArXi:2510.19669v4 Announce Type: replace Recent reasoning Large Language Models (LLMs) nstrate remarkable problem-solving abilities but often generate long thinking traces whose utility is unclear. Our work aims to improve their efficiency, enabling them to reach high performance without overthinking. First, we analyze the entropy of token probabilities in reasoning traces.